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Gamma-hydroxybutyric acid (GHB) is a drug-of-abuse that has recently become associated with drug-facilitated sexual assault, known as date rape. For this reason the drug is commonly found 'spiked' in alcoholic beverages. When GHB is in solution it may undergo conversion into the corresponding lactone, Gamma-butyrolactone (GBL). Studies have been carried out to determine the detection limits of GHB and GBL in various solutions by Raman spectroscopy and to monitor the interconversion of GHB and GBL in solution with different pH conditions and temperature. In this study, a portable Raman spectrometer was used to study the interconversion of GHB and GBL in water and ethanol solutions as a function of pH, time, and temperature. The aim of this was to determine the optimum pH range for conversion in order to relate this to the pH ranges that the drug is likely to be subjected to, first in spiked beverages and secondly after ingestion in the digestive system. The aim was also to identify a timescale for this conversion in relation to possible scenarios, for example if GHB takes a number of hours to convert to GBL, it is likely for the beverage to be ingested before esterification can take place. GHB and GBL were then spiked into a selection of beverages of known pH in order to study the stability of GHB and GBL in real systems.

Demographic-based identification plays an active role in the field of face identification. Over the past decade, machine learning algorithms have been used to investigate challenges surrouding ethnic classification for specific populations, such as African, Asian and Caucasian people. Ethnic classification for individuals of South Asian, Pakistani heritage, however, remains to be addressed.The present paper addresses a two-category (Pakistani Vs Non-Pakistani) classification task from a novel, purpose-built dataset.To the best of our knowledge, this work is the first to report a machine learning ethnic classification task with South Asian (Pakistani) faces. We conduted a series of experiments using deep learning algorithms (ResNet-50, ResNet-101 and ResNet-152) for feature extraction and a linear support vector machine (SVM) for classification. The experimental results demonstrate ResNet-101 achieves the highest performance accuracy of 99.2% for full-face ethnicity classification, followed closely by 91.7% and 95.7% for the nose and mouth respectively.

Custody images are a standard feature of everyday Policing and are commonly used during investigative work to establish whether the perpetrator and the suspect are the same. The process of identification relies heavily on the quality of a custody image because a low-quality image may mask identifying features. With an increased demand for high quality facial images and the requirement to integrate biometrics and machine vision technology to the field of face identification, this research presents an innovative image capture and biometric recording system called the Halo. Halo is a pioneering system which (1) uses machine vision cameras to capture high quality facial images from 8 planes of view (including CCTV simulated), (2) uses high quality video technology to Original Research Article

The nose is the most central feature on the face which is known to exhibit both gender and ethnic differences. It is a robust feature, invariant to expression and known to contain depth information. In this paper we address the topic of binary ethnicity classificiation from images of the nose, using a novel dataset of South Asian, Pakistani images. To the best of our knowledge, we are one of the first to attempt demographic (ethnicity) based identification based solely on information from the nose. A two-category (Pakistani vs Non-Pakistani) task was used in combination with Deep learning (ResNet) based and VGG-based pre-trained models. A series of experiments were conducted usingResNet-50, ResNet-101, ResNet-152, VGG-Face, VGG-16 and VGG-19, for feature extraction and a Linear Support Vector Machine for classification. The experimental results demonstrate ResNet-50 achieves the highest performance accuracy of 94.1%. In comparison, the highest score for the VGG-based models (VGG-16) was 90.8%. These results demonstrate that information from the nose is sufficient for deep learning models to achieve >90% accuracy on judgements of ethnicity.